Artificial Intelligence Integration in Neonatal Surgery for Enhancing Precision and Outcomes through Advanced Algorithmic Approaches

Abstract

Neonatal surgery has been transformed by artificial intelligence (AI), which has improved intraoperative decision-making, surgical planning, and diagnostic precision. By assessing popular machine learning and deep learning methods like Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Reinforcement Learning models, this study investigates the incorporation of AI in newborn surgical operations. Analysing existing AI-assisted newborn surgical systems, determining their shortcomings, and suggesting an ideal hybrid AI model that combines Reinforcement Learning and Transformer-based vision models for real-time decision support are all part of the research process. Using important performance measures such as accuracy, precision, recall, computational economy, and real-time adaptation, the suggested model is compared to current techniques. According to experimental results, compared to traditional AI-assisted approaches, surgical precision can be improved by 15-20%, anomaly detection can be improved by 25%, and surgical time can be decreased by 10%. The results highlight the potential of AI-powered neonatal surgery to improve patient outcomes, reduce risks, and establish a new standard for paediatric surgery.

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